Highly Robust Visual Place Recognition Through Spatial Matching of CNN Features

Abstract

We revise, improve and extend the system previously introduced by us and named SSM-VPR (Semantic and Spatial Matching Visual Place Recognition), largely boosting its performance above the current state of the art. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. It consists of two stages: given a query image of a place, (1) a list of candidates is selected from a database of places and (2) the candidates are geometrically compared with the query. The comparison is made by matching CNN features and, equally important, their spatial locations, selecting the best candidate as the recognized place. The performance of the system is maximized by finding optimal image resolutions during the second stage and by exploiting temporal correlation between consecutive frames in the employed datasets.